在下肢同度收缩过程中检测肌肉疲劳任务:机器学习方法
Jiaqi Sun1, Cheng Zhang1, Guangda Liu1
1College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China.
Frontiers in physiology
|April 4, 2025
概括
这项研究引入了一种机器学习方法,使用电肌图 (EMG) 信号检测肌肉疲劳. 该方法实现了99.8%的准确性,超过了现有的疲劳检测方法.
科学领域:
- 生物医学工程 生物医学工程
- 运动科学 运动科学 运动科学
- 康复技术 康复技术 康复技术
背景情况:
- 肌肉疲劳是运动引起的疲劳的一个关键指标.
- 电肌图 (EMG) 对于监测肌肉活动和评估疲劳至关重要.
- 自动检测肌肉疲劳对于性能和安全至关重要.
研究的目的:
- 开发和验证一种机器学习方法,用于使用EMG信号自动检测肌肉疲劳.
- 为了提高肌肉疲劳评估的准确性和效率.
- 为在各种环境中提供监测疲劳的工具.
主要方法:
- 在同度收缩期间,从下肢肌肉收集EMG信号.
- 内在模式函数 (IMF) 通过改进的互补集合实证模式分解适应噪声 (ICEEMDAN) 来提取.
- 一个机器学习模型,结合支持向量机 (SVM) 和ICEEMDAN,被用来进行疲劳分类,在使用t-SNE减少维度后.
主要成果:
- 显著相关的EMG信号特征与增加的肌肉疲劳.
- 拟议的SVM和ICEEMDAN模型实现了高分类准确率99.8%.
- 与当前最先进的疲劳检测技术相比,该方法显示出更高的性能.
结论:
- 开发的机器学习策略对于自动检测肌肉疲劳非常有效.
- 这种方法提供了一种有效可靠的方法来监测训练,康复和职业环境中的疲劳.
- 这种高精度表明,体育科学和职业健康领域的实际应用有很大的潜力.
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